🧑‍🔬 Junlan Feng

🏛️ Affiliations

🏢China Mobile Research InstituteBeijing · China2016–2026 · 112 paperscurrent🏢China MobileBeijing · China2016–2026 · 31 paperscurrent🏢AT&TBedminster · United States2004–2021 · 20 papers🎓Tsinghua UniversityBeijing · China2021–2024 · 12 papers🎓Nanjing UniversityNanjing · China2024–2025 · 2 papers🏥Shanghai Tenth People's HospitalShanghai · China2020–2021 · 2 papers🎓Tongji UniversityShanghai · China2020–2021 · 2 papers🏛️Chinese Academy of SciencesBeijing · China2002–2016 · 2 papers🎓Shanghai Jiao Tong UniversityShanghai · China2014–2015 · 2 papers🏥Shanghai Sixth People's HospitalShanghai · China2014–2015 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. Robust Sentiment Detection on Twitter from Biased and Noisy Data (2010 · 955 citations)
   Barbosa and Feng address sentiment classification of tweets. Tweets are too short for the classic n-gram models built for long texts such as reviews and news, and hand-labelled training data is expensive. They use a two-stage pipeline: first separate subjective from objective tweets, then label subjective ones as…
2. Thinking Alignment of Scenario-Oriented User Simulation (2026 · 1 citation)
   LLM-based user simulators, whether they prompt a model to role-play or fine-tune it on real user turns, usually reproduce only what users type.
3. AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs (2026)
   LLMs increasingly write executable Python code for alpha mining rather than symbolic factor formulas. This adds expressiveness but also hidden risks: look-ahead bias (using future data) and bugs in stateful logic. Existing alpha benchmarks mostly score backtest profitability, which can reward leaked information or…
4. ChildEval:WHEN LARGE LANGUAGE MODELS MEET CHILDREN'S PERSONALITIES (2026)
   LLM-based chatbots are increasingly used as companions for young children. Yet nobody has systematically tested whether these models pick up on and respect a child's preferences. Existing personalization benchmarks mostly target adults, and child-focused ones cover only narrow aspects such as safety or style…
5. PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning (2026)
   LLM translation trained with reinforcement learning (e.g., GRPO) suffers from two problems. Its Monte Carlo group baselines are noisy, and its huge output space rewards broad exploration but gives little signal for small local fixes. PEGRL addresses this by treating translation as a two-step workflow: a draft…
6. Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning (2026)
   The paper addresses continual learning (CL) for large language models. Full retraining is too costly at this scale, and most existing CL methods focus only on preventing catastrophic forgetting, without exploiting knowledge that tasks could share. The authors first observe that better-performing CL models produce…

All works

8 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

1. Robust Sentiment Detection on Twitter from Biased and Noisy DataInternational Conference on Computational Linguistics (COLING)Luciano Barbosa, Junlan Feng2010 · 955 citations
2. Thinking Alignment of Scenario-Oriented User SimulationAnnual Meeting of the Association for Computational Linguistics (ACL)Xiaoting Wu, Yi Huang, Chunyang Gao, et al.2026 · 1 citation
3. AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMsFindings of the Association for Computational LinguisticsSichun Luo, Yi Huang, Shichang Meng, et al.2026
4. ChildEval:WHEN LARGE LANGUAGE MODELS MEET CHILDREN'S PERSONALITIESFindings of the Association for Computational LinguisticsYanyan Luo, Xue Han, Chunxu Zhao, et al.2026
5. PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement LearningFindings of the Association for Computational LinguisticsYunzhi Shen, Hao Zhou, Xin Huang, et al.2026
6. Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Yitong Wang, Xue Han, WenChun Gao, et al.2026
7. D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetZhe Yang et al.2026
8. A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetZhe Yang et al.2026

🤝 Co-authors here

12 co-authors with a page, the most papers together first.

• Xue Han ×3
• Lijun Mei ×2
• Qian Hu ×2
• Yi Huang ×2
• Hao Zhou
• Mukai Li
• Qi Liu
• Shujian Huang
• Sichun Luo
• Xin Huang
• Yitong Wang
• Ziqing Wang

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×3: Findings of the Association for Computational Linguistics 2026 ×3
• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026 ×2
• Findings of the Association for Computational Linguistics: ACL 2026 ×2
• International Conference on Computational Linguistics (COLING): COLING 2010

🔗 Cited by

Papers of our library cite their works 91 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated 1 November 2026

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